4: Lidar data with Python

Due before 11:59pm on May 4

10 points

1. Create a public GitHub repository called gisc606-lab4 in your GitHub account using github/codespaces-jupyter as a template repository: https://github.com/github/codespaces-jupyter

2. Open your repository in a GitHub Codespace

3. Create a Jupyter Notebook for and complete each of the following lessons from Earth Lab CU Boulder's Intermediate Earth Data Science textbook

   1. Introduction to Light Detection and Ranging (Lidar) Remote Sensing Data: intro_lidar.ipynb

   2. Get to know Lidar (Light Detection and Ranging) Point Cloud Data: point_cloud_data.ipynb

   3. How lidar point clouds are converted to raster data formats: lidar_conversion.ipynb

   4. Canopy Height Models, Digital Surface Models & Digital Elevation Models: chm.ipynb

   5. Compare Lidar With Human Measured Tree Heights: intro_to_comparing.ipynb

   6. Extract Raster Values at Point Locations in Python: extract_values_at_point.ipynb

   7. Compare Lidar to Measured Tree Height: compare_lidar_to_measurements.ipynb

   8. Use Regression Analysis to Explore Data Relationships & Bad Data: regression.ipynb

4. Commit the changes to your repository

5. Once you are complete with all the lesson notebooks, navigate to Lab 4 assignment in Canvas

6. Input the url of your gisc606-lab4 GitHub repository then click submit

7. Optional: Lab 4 extra credit opportunity in Canvas (worth 5 additional points)